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... image_embeds=image_emb, |
... negative_image_embeds=zero_image_emb, |
... height=768, |
... width=768, |
... num_inference_steps=150, |
... ).images[0] |
>>> image.save("starry_cat.png") KandinskyV22Img2ImgPipeline class diffusers.KandinskyV22Img2ImgPipeline < source > ( unet: UNet2DConditionModel scheduler: DDPMScheduler movq: VQModel ) Parameters scheduler (DDIMScheduler) — |
A scheduler to be used in combination with unet to generate image latents. unet (UNet2DConditionModel) — |
Conditional U-Net architecture to denoise the image embedding. movq (VQModel) — |
MoVQ Decoder to generate the image from the latents. Pipeline for image-to-image generation using Kandinsky This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods the |
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) __call__ < source > ( image_embeds: Union image: Union negative_image_embeds: Union height: int = 512 width: int = 512 num_inference_steps: int = 100 guidance_scale: float = 4.0 strength: float = 0.3 num_images_per_prompt: int = 1 generator: Union = None output_type: Optional = 'pil' return_dict: bool = True callback_on_step_end: Optional = None callback_on_step_end_tensor_inputs: List = ['latents'] **kwargs ) → ImagePipelineOutput or tuple Parameters image_embeds (torch.FloatTensor or List[torch.FloatTensor]) — |
The clip image embeddings for text prompt, that will be used to condition the image generation. image (torch.FloatTensor, PIL.Image.Image, np.ndarray, List[torch.FloatTensor], List[PIL.Image.Image], or List[np.ndarray]) — |
Image, or tensor representing an image batch, that will be used as the starting point for the |
process. Can also accept image latents as image, if passing latents directly, it will not be encoded |
again. strength (float, optional, defaults to 0.8) — |
Conceptually, indicates how much to transform the reference image. Must be between 0 and 1. image |
will be used as a starting point, adding more noise to it the larger the strength. The number of |
denoising steps depends on the amount of noise initially added. When strength is 1, added noise will |
be maximum and the denoising process will run for the full number of iterations specified in |
num_inference_steps. A value of 1, therefore, essentially ignores image. negative_image_embeds (torch.FloatTensor or List[torch.FloatTensor]) — |
The clip image embeddings for negative text prompt, will be used to condition the image generation. height (int, optional, defaults to 512) — |
The height in pixels of the generated image. width (int, optional, defaults to 512) — |
The width in pixels of the generated image. num_inference_steps (int, optional, defaults to 100) — |
The number of denoising steps. More denoising steps usually lead to a higher quality image at the |
expense of slower inference. guidance_scale (float, optional, defaults to 4.0) — |
Guidance scale as defined in Classifier-Free Diffusion Guidance. |
guidance_scale is defined as w of equation 2. of Imagen |
Paper. Guidance scale is enabled by setting guidance_scale > 1. Higher guidance scale encourages to generate images that are closely linked to the text prompt, |
usually at the expense of lower image quality. num_images_per_prompt (int, optional, defaults to 1) — |
The number of images to generate per prompt. generator (torch.Generator or List[torch.Generator], optional) — |
One or a list of torch generator(s) |
to make generation deterministic. output_type (str, optional, defaults to "pil") — |
The output format of the generate image. Choose between: "pil" (PIL.Image.Image), "np" |
(np.array) or "pt" (torch.Tensor). return_dict (bool, optional, defaults to True) — |
Whether or not to return a ImagePipelineOutput instead of a plain tuple. callback_on_step_end (Callable, optional) — |
A function that calls at the end of each denoising steps during the inference. The function is called |
with the following arguments: callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict). callback_kwargs will include a list of all tensors as specified by |
callback_on_step_end_tensor_inputs. callback_on_step_end_tensor_inputs (List, optional) — |
The list of tensor inputs for the callback_on_step_end function. The tensors specified in the list |
will be passed as callback_kwargs argument. You will only be able to include variables listed in the |
._callback_tensor_inputs attribute of your pipeline class. Returns |
ImagePipelineOutput or tuple |
Function invoked when calling the pipeline for generation. Examples: KandinskyV22Img2ImgCombinedPipeline class diffusers.KandinskyV22Img2ImgCombinedPipeline < source > ( unet: UNet2DConditionModel scheduler: DDPMScheduler movq: VQModel prior_prior: PriorTransformer prior_image_encoder: CLIPVisionModelWithProjection prior_text_encoder: CLIPTextModelWithProjection prior_tokenizer: CLIPTokenizer prior_scheduler: UnCLIPScheduler prior_image_processor: CLIPImageProcessor ) Parameters scheduler (Union[DDIMScheduler,DDPMScheduler]) — |
A scheduler to be used in combination with unet to generate image latents. unet (UNet2DConditionModel) — |
Conditional U-Net architecture to denoise the image embedding. movq (VQModel) — |
MoVQ Decoder to generate the image from the latents. prior_prior (PriorTransformer) — |
The canonincal unCLIP prior to approximate the image embedding from the text embedding. prior_image_encoder (CLIPVisionModelWithProjection) — |
Frozen image-encoder. prior_text_encoder (CLIPTextModelWithProjection) — |
Frozen text-encoder. prior_tokenizer (CLIPTokenizer) — |
Tokenizer of class |
CLIPTokenizer. prior_scheduler (UnCLIPScheduler) — |
A scheduler to be used in combination with prior to generate image embedding. prior_image_processor (CLIPImageProcessor) — |
A image_processor to be used to preprocess image from clip. Combined Pipeline for image-to-image generation using Kandinsky This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods the |
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) __call__ < source > ( prompt: Union image: Union negative_prompt: Union = None num_inference_steps: int = 100 guidance_scale: float = 4.0 strength: float = 0.3 num_images_per_prompt: int = 1 height: int = 512 width: int = 512 prior_guidance_scale: float = 4.0 prior_num_inference_steps: int = 25 generator: Union = None latents: Optional = None output_type: Optional = 'pil' callback: Optional = None callback_steps: int = 1 return_dict: bool = True prior_callback_on_step_end: Optional = None prior_callback_on_step_end_tensor_inputs: List = ['latents'] callback_on_step_end: Optional = None callback_on_step_end_tensor_inputs: List = ['latents'] ) → ImagePipelineOutput or tuple Parameters prompt (str or List[str]) — |
The prompt or prompts to guide the image generation. image (torch.FloatTensor, PIL.Image.Image, np.ndarray, List[torch.FloatTensor], List[PIL.Image.Image], or List[np.ndarray]) — |
Image, or tensor representing an image batch, that will be used as the starting point for the |
process. Can also accept image latents as image, if passing latents directly, it will not be encoded |
again. negative_prompt (str or List[str], optional) — |
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored |
if guidance_scale is less than 1). num_images_per_prompt (int, optional, defaults to 1) — |
The number of images to generate per prompt. guidance_scale (float, optional, defaults to 4.0) — |
Guidance scale as defined in Classifier-Free Diffusion Guidance. |
guidance_scale is defined as w of equation 2. of Imagen |
Paper. Guidance scale is enabled by setting guidance_scale > 1. Higher guidance scale encourages to generate images that are closely linked to the text prompt, |
usually at the expense of lower image quality. strength (float, optional, defaults to 0.3) — |
Conceptually, indicates how much to transform the reference image. Must be between 0 and 1. image |
will be used as a starting point, adding more noise to it the larger the strength. The number of |
denoising steps depends on the amount of noise initially added. When strength is 1, added noise will |
be maximum and the denoising process will run for the full number of iterations specified in |
num_inference_steps. A value of 1, therefore, essentially ignores image. num_inference_steps (int, optional, defaults to 100) — |
The number of denoising steps. More denoising steps usually lead to a higher quality image at the |
expense of slower inference. height (int, optional, defaults to 512) — |
The height in pixels of the generated image. width (int, optional, defaults to 512) — |
The width in pixels of the generated image. prior_guidance_scale (float, optional, defaults to 4.0) — |
Guidance scale as defined in Classifier-Free Diffusion Guidance. |
guidance_scale is defined as w of equation 2. of Imagen |
Paper. Guidance scale is enabled by setting guidance_scale > 1. Higher guidance scale encourages to generate images that are closely linked to the text prompt, |
usually at the expense of lower image quality. prior_num_inference_steps (int, optional, defaults to 100) — |
The number of denoising steps. More denoising steps usually lead to a higher quality image at the |
expense of slower inference. generator (torch.Generator or List[torch.Generator], optional) — |
One or a list of torch generator(s) |
to make generation deterministic. latents (torch.FloatTensor, optional) — |
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image |
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents |
tensor will ge generated by sampling using the supplied random generator. output_type (str, optional, defaults to "pil") — |
The output format of the generate image. Choose between: "pil" (PIL.Image.Image), "np" |
(np.array) or "pt" (torch.Tensor). callback (Callable, optional) — |
A function that calls every callback_steps steps during inference. The function is called with the |
following arguments: callback(step: int, timestep: int, latents: torch.FloatTensor). callback_steps (int, optional, defaults to 1) — |
The frequency at which the callback function is called. If not specified, the callback is called at |
every step. return_dict (bool, optional, defaults to True) — |
Whether or not to return a ImagePipelineOutput instead of a plain tuple. Returns |
ImagePipelineOutput or tuple |
Function invoked when calling the pipeline for generation. Examples: Copied from diffusers import AutoPipelineForImage2Image |
import torch |
import requests |
from io import BytesIO |
from PIL import Image |
import os |
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